Average

Average#

The average of random variables \(X = [X_1, X_2, \ldots, X_n]\) is a type of sums notably,

\[ \bar X \equiv \frac{1}{n}S = \frac{1}{n} \sum_{k=1}^n X_k \]

  • This is very different from the sum is that \(\bar X\) is continuous thus more useful in some contexts.

  • Strictly textbook, this is identical to the sample average which is only correct in the context of samples (we will get to samples later). Since all \(X_k\) are random variables, \(\bar X\) is also a random variable hence should never be called sample average when dealing with random variables.

Expectation : Using the expectation of sums,

$$
E(\bar X) = \frac{1}{n}\sum_{k=1}\mu_k
$$

Variance : $\( \text{Var}(\bar X) = \frac{\sigma^2}{n} \)$

Unbiased Estimator of the Mean : \(\bar X\) is the unbiased estimator of the expectation \(\mu\).

$$
E(\bar X) = \mu
$$

Central Limit Theorem#

At the limit of large \(n\),

\[ \bar X \to \text{Normal}(\mu,~ \sigma^2/n) \]
\[ P(\bar X \le x) \approx \Phi\left(\frac{x-\mu}{\sigma / \sqrt{n}}\right) \]

Confidence Intervals#

As \(\bar X\) is most commonly useful for the unbiased estimator of the mean, thus it is also commonly used to generate the confidence interval of the estimate.

Let \(p\) be the chance for \(X\) to be in the confidence interval \(c\) away from the mean \(\mu\) :

\[ P(\bar X \in \mu \pm c) = P\left( \mu \in \bar X \pm c\right) = P\left( \abs{\bar X - \mu} \le c \right) \]

  • We take preference of the RHS for notation here on since it’s more algebraically useful.

  • The center expression is more accurate as it implies “the chance that the random interval \(\bar X \pm c\) contains the mean \(\mu\)”.

  • More often, the numerical value is used post-calculation. This value is called the p-value

At large \(n\), CLT holds so that

\[\begin{split} \begin{align*} p &= P\left( \abs{\bar X - \mu} \le c \right) \\ &= P(\bar X \le \mu + c) - P(\bar X \le \mu - c)\\ &= \Phi\left(\frac{c}{\sigma/\sqrt{n}}\right) - \Phi\left(\frac{c-2\mu}{\sigma/\sqrt{n}}\right) \end{align*} \end{split}\]

Number of SDs : More conveniently we can replace \(c\) with the number \(z\) of SDs away from the mean,

$$
p \equiv P\left( \abs{\bar X - \mu} \le z\frac{\sigma}{\sqrt{n}} \right); \qquad z = \frac{c}{\sigma/\sqrt{n}}
$$

The left bound of the interval is at $\mu - z \cdot \sigma/\sqrt{n}$ where we know the CDF to that bound is,

$$
\Phi\left(-z\right) = \frac{1-p}{2}
$$

To solve for $z$, we need the inverse CDF of the standard normal,

$$
\boxed{z = \abs{\Phi^{-1}\left(\frac{1-p}{2}\right)}}
$$

Alternatively, we can use the right bound of the interval to get,

$$
\boxed{z = \Phi^{-1}\left(p + \frac{1-p}{2}\right)}
$$

Sample Average#

Now let \(X_1, X_2, \ldots, X_n\) be an array of independent samples of the random variable \(X\) with mean \(\mu\) and variance \(\sigma^2\).

Law of Averages#

\[ P(\abs{\bar X - \mu} < \epsilon) \rightarrow 1, \quad \text{as } n \to \infty \]

Proof : Since,

$$
P(\abs{\bar X - \mu} < \epsilon) = 1 - P(\abs{\bar X - \mu} \ge \epsilon)
$$

By Chebyshev's inequality,

$$
P(\abs{\bar X - \mu} \ge \epsilon) = \frac{\sigma^2}{n \epsilon^2} \to 0, \quad \text{as } n \to \infty
$$
Contents